We sought to study the associations between plasma metabolites in the tryptophan-kynurenine pathway and the risk of progression to end-stage kidney disease (ESKD) in patients with type 2 diabetes.
Plasma tryptophan, kynurenine, 3-hydroxykynurenine, kynurenic acid, and xanthurenic acid concentrations were measured in discovery (n = 1,915) and replication (n = 346) cohorts. External validation was performed in Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n = 1,312). The primary outcome was a composite of incident ESKD (progression to estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2, sustained dialysis, or renal death). The secondary outcome was annual eGFR decline.
In the discovery cohort, tryptophan was inversely associated with risk for ESKD, and kynurenine-to-tryptophan ratio (KTR) was positively associated with risk for ESKD after adjustment for clinical risk factors, including baseline eGFR and albuminuria (adjusted hazard ratios [HRs] 0.62 [95% CI 0.51, 0.75] and 1.48 [1.20, 1.84] per 1 SD). High levels of kynurenic acid and xanthurenic acid were associated with low risks of ESKD (0.74 [0.60, 0.91] and 0.74 [0.60, 0.91]). Consistently, high levels of tryptophan, kynurenic acid, and xanthurenic acid were independently associated with a slower eGFR decline, while a high KTR was predictive of a faster eGFR decline. Similar outcomes were obtained in the replication cohort. Furthermore, the inverse association between kynurenic acid and risk of ESKD was externally validated in CRIC participants with diabetes (adjusted HR 0.78 [0.65, 0.93]).
Accelerated catabolism of tryptophan in the kynurenine pathway may be involved in progressive loss of kidney function. However, shunting the kynurenine pathway toward the kynurenic acid branch may potentially slow renal progression.
Introduction
Diabetic kidney disease (DKD) affects 20–40% of patients with diabetes, and it is the leading cause of end-stage kidney disease (ESKD) in many countries (1,2). Of note, Asian individuals with diabetes are more susceptible to kidney impairment than their European counterparts (3). Intensive control of traditional risk factors and treatment with novel renal protective agents may markedly reduce the risk for adverse renal outcomes. However, residual risks remain high, and a large number of patients with diabetes still progress to ESKD, even in clinical trial settings (4). Hence, an in-depth understanding of the pathophysiological mechanisms of progressive DKD is required to shed light on new intervention targets.
The kynurenine pathway is the primary route for tryptophan catabolism. In addition to serving as integrators of energy metabolism and maintaining homeostasis of NAD, metabolites in this pathway are involved in the regulation of inflammation and the immune response (5). Catabolism of tryptophan to kynurenine by tryptophan 2,3-dioxygenase in liver or indoleamine 2,3-dioxygenase (IDO) in various other tissues is the first and rate-limiting step in the kynurenine pathway. The kynurenine-to-tryptophan ratio (KTR) is therefore considered a proxy for kynurenine pathway activity because production of kynurenine is mainly determined by IDO (6). The biochemical pathway downstream of kynurenine is divided into three branches (Supplementary Fig. 1). The NAD branch sequentially catabolizes kynurenine to 3-hydroxykynurenine and oxidized NAD. The picolinic acid branch converts 3-hydroxyanthranilic acid to picolinic acid. The kynurenic acid branch produces kynurenic acid from kynurenine and xanthurenic acid from 3-hydroxykynurenine.
Dysregulation of the tryptophan-kynurenine pathway has been associated with cardiovascular disease (7), cancer (8), and neurological disorders (9). Emerging evidence suggests that dysregulation of the kynurenine pathway may also be involved in the development of chronic kidney disease (CKD) (10). For example, a prospective study found that plasma levels of kynurenine and KTR predicted new-onset CKD (11,12). We hereby sought to study whether plasma metabolites in the tryptophan-kynurenine pathway (tryptophan, kynurenine, KTR, 3-hydroxykynurenine, kynurenic acid, and xanthurenic acid) may predict the risk for progression to ESKD in patients with type 2 diabetes.
Research Design and Methods
We adopted a discovery, replication, and validation approach in this prospective cohort study.
Discovery Cohort
The discovery substudy was nested in the Singapore Study of Macro-Angiopathy and Micro-Vascular Reactivity in Type 2 Diabetes (SMART2D) cohort, an ongoing study on micro- and macrovascular complications in Southeast Asian individuals with type 2 diabetes. Briefly, 2,057 outpatients with type 2 diabetes were recruited from a regional hospital and a primary care facility in northern Singapore between August 2011 and March 2014 (13). Patients receiving active treatments for overt infection, autoimmune disease, or cancer and those with kidney disease attributable to specific causes were excluded from enrollment. Participants were followed by reviewing electronic medical records and invited for in-person research visits every 3 years. Data from routine clinical care and research visits were combined into one database to ascertain clinical outcomes. We excluded 114 participants with advanced CKD (baseline estimated glomerular filtration rate [eGFR] <30 mL/min/1.73 m2) and 28 participants with no plasma samples available. A total of 1,915 participants were included in this study.
Replication Cohort
The replication substudy was nested in the Singapore Khoo Teck Puat Hospital Diabetic Kidney Disease (KTPH-DKD) cohort, which consecutively recruited patients with type 2 diabetes who visited the hospital between March 2004 and October 2017. Patients with clinically manifested infection, active treatment of autoimmune disease or cancer, and kidney disease attributable to other known specific causes were excluded. Participants were followed by reviewing electronic medical records and linkage with national disease registries (14). We considered 1,539 participants with baseline eGFR ≥30 mL/min/1.73 m2 recruited from January 2011 onward as candidates for the current study because the centralized electronic medical records were accessible from that time. Because of funding limitations, we randomly selected 346 participants for this replication substudy without considering stratification by age, sex, and baseline kidney function.
Validation of the Association Between Plasma Kynurenic Acid and Incident ESKD in the Chronic Renal Insufficiency Cohort
The Chronic Renal Insufficiency Cohort (CRIC) is a multicenter, prospective study of patients with CKD in the U.S. (15). Of 3,423 participants with plasma kynurenic acid measured in an earlier study (16), all those with diabetes and baseline eGFR ≥30 mL/min/1.73 m2 were included in this analysis (n = 1,312).
The discovery and replication substudies were approved by Singapore National Healthcare Group Ethics Review Committee. Each participant gave written consent. Consent for the current study from CRIC participants was waived by National Institute of Diabetes and Digestive and Kidney Diseases.
Clinical Outcomes
The primary outcome was incident ESKD defined as a composite of 1) progression to eGFR <15 mL/min/1.73 m2 with at least one confirmation eGFR measurement 3 months apart, 2) sustained dialysis for >3 months, or 3) death attributable to renal causes, whichever occurred first. Deaths were identified from electronic medical records and by linkage with the national death registry. Renal death was identified based on primary diagnosis on the death certificate. The follow-up was censored at 30 November 2019.
The secondary outcome was annual eGFR decline estimated by mixed linear model. Only participants with at least three eGFR readings in >2 years of follow-up were included in this analysis. We defined rapid kidney function decline as an eGFR decline of ≥5 mL/min/1.73 m2 per year (17).
In the CRIC, incident ESKD was defined as a composite of progression to eGFR <15 mL/min/1.73 m2, chronic dialysis, or kidney transplant. We restricted the follow-up duration to 15 years because limited data were available beyond this duration.
Clinical and Biochemical Variables
Age, sex, smoking status, and ethnicity were self-reported. Atherosclerotic cardiovascular disease (ASCVD) history, which included nonfatal myocardial infarction and stroke, was self-reported and ascertained by review of medical records after cohort enrollment. Medication use at baseline was obtained from the medication dispensary database. No participants were receiving sodium–glucose cotransporter 2 inhibitor treatment at baseline. Blood pressure was measured three times, and the mean was used in the discovery cohort. However, it was measured once in the replication cohort. Mean arterial pressure was calculated as (systolic blood pressure + 2 × diastolic blood pressure) / 3. HbA1c was measured by a point-of-care immunoassay analyzer (DCA Vantage Analyzer; Siemens, Munich, Germany) in the discovery cohort and by immunoturbidimetric method (Cobas Integra 800 Chemistry Analyzer; Roche, Basel, Switzerland) in the replication cohort. Creatinine was quantified by an enzymatic method that was traceable to isotope dilution mass spectrometry reference in both cohorts. The eGFR was calculated based on serum creatinine using the 2009 Chronic Kidney Disease Epidemiology Collaboration equation. Urinary albumin was quantified by an immunoturbidimetric assay (cobas c; Roche Diagnostics, Mannheim, Germany). Plasma C-reactive protein was measured by an immunoassay kit (R&D Systems, Minneapolis, MN).
Measurements of Plasma Metabolites in the Tryptophan-Kynurenine Pathway by Liquid Chromatography-Mass Spectrometry
For both the discovery and replication cohorts, plasma specimens were collected after overnight fasting and stored at −80°C. Samples used for the current study underwent only one freeze-thaw cycle. The samples were coded and randomized in each cohort to reduce run order effects. Metabolites were extracted using methanol and derivatized by 3.0 mol/L HCl in butanol. Compound quantification was performed on an ABSciex 5500 triple QTrap coupled to an Agilent 1290 liquid chromatography system (Agilent Technologies, Santa Clara, CA). Details of liquid chromatography-mass spectrometry methods and quality control approaches are described in the Supplementary Methods.
Statistical Analysis
Clinical and biochemical variables are presented as mean ± SD, median (interquartile range [IQR]), or proportion. Missing values were <0.5% for all variables and were handled by listwise deletion. Given that 1) metabolites, including kynurenine and kynurenic acid, are uremic solutes with plasma retention in patients with renal insufficiency (16,18) (Supplementary Table 1) and 2) baseline renal impairment was the most important determinant for incident ESKD, we regressed the log-transformed values of each metabolite on eGFR (metabolite value as dependent variable and eGFR as independent variable) and saved the residual as a new variable. The residuals were standardized to a mean of 0 and an SD of 1 (z-score). KTR was handled by the same approach. Thus, five plasma metabolites and KTR might be considered eGFR-independent exposures in the statistical models (Supplementary Table 2).
Cox regression models were fitted to study the association of each metabolite and KTR with risk for progression to ESKD. Covariates were a priori determined by biological plausibility. Age, sex, ethnicity (Chinese as reference), active smoking (yes or no), ASCVD history (yes or no), use of renin-angiotensin system blockers (yes or no), BMI, diabetes duration, HbA1c, mean arterial pressure, baseline eGFR, and log-transformed urine albumin-to-creatinine ratio (ACR) were adjusted in the multivariable model. The proportional hazard assumption was assessed by Schoenfeld residuals and by modeling metabolite × time as a multiplicative interaction term.
We performed several sensitivity analyses. First, we directly modeled the association between plasma metabolites and ESKD risk by adjusting baseline eGFR as a covariate. Second, we redefined the renal outcome as a composite of ESKD and doubling of creatinine (i.e., 57% eGFR decline extrapolated from eGFR slope) (16). Third, we used competing risk regression (Fine and Gray subdistribution) to study the association between metabolites and incident ESKD by modeling nonrenal death as a competing risk. Fourth, we stratified participants by CKD status, albuminuria category, and ethnicity to examine the association between metabolites and ESKD in subgroups. Finally, we additionally adjusted for C-reactive protein, aspirin use, and antidiabetic medications in the multivariable model in the discovery cohort.
We applied linear regression to study the relationship between plasma metabolites and eGFR slope, in which annual change in eGFR was the dependent variable and plasma metabolite level the independent variable. Furthermore, we used logistic regression to study the association between plasma metabolites and rapid kidney function decline as a binary outcome. We studied the association between plasma kynurenic acid and ESKD in CRIC using the same approach as that in discovery and replication cohorts.
Data analyses were performed using SPSS version 27 and R version 3.4.2 software. A two-sided P < 0.05 was considered statistically significant.
Data and Resource Availability
Data used in this study are not publicly available. However, anonymized data may be available upon reasonable request from the corresponding author.
Results
Participant Characteristics
A total of 1,915 and 346 participants were included in discovery and replication substudies, respectively. Clinical and biochemical characteristics of participants are presented in Table 1. Compared with the discovery cohort, participants in the replication cohort had a lower eGFR, higher urine ACR, and higher HbA1c. They were also more likely to have a history ASCVD and to be on insulin treatment.
Participant baseline clinical and biochemical characteristics in the discovery (SMART2D) and replication (KTPH-DKD) cohorts
. | Discovery . | Replication . | ||
---|---|---|---|---|
. | n . | Value . | n . | Value . |
Age (years) | 1,915 | 57.1 ± 10.8 | 346 | 54.7 ± 12.6 |
Male sex (%) | 1,915 | 50.2 | 346 | 59.2 |
Ethnicity | 1,915 | 346 | ||
Chinese | — | 51.2 | — | 54.6 |
Malay | — | 21.7 | — | 27.5 |
Asian Indian | — | 27.2 | — | 17.9 |
ASCVD history (%) | 1,914 | 7.7 | 346 | 15.0 |
Active smokers (%) | 1,911 | 8.7 | 346 | 14.7 |
Diabetes duration (years) | 1,906 | 11.0 ± 8.9 | 345 | 11.8 ± 8.5 |
BMI (kg/m2) | 1,911 | 27.7 ± 5.2 | 346 | 27.3 ± 5.7 |
HbA1c (%) | 1,915 | 7.8 ± 1.3 | 346 | 8.9 ± 2.2 |
HbA1c (mmol/mol) | — | 62 ± 10 | — | 74 ± 18 |
Blood pressure (mmHg) | 1,915 | 340 | ||
Systolic | — | 140 ± 18 | — | 138 ± 19 |
Diastolic | — | 79 ± 9 | — | 79 ± 12 |
Mean arterial pressure | — | 99 ± 11 | — | 98 ± 12 |
eGFR (mL/min/1.73 m2) | 1,915 | 89 ± 23 | 346 | 84 ± 30 |
Urine ACR (mg/g), median (IQR) | 1,904 | 21 (6–83) | 327 | 55 (12–375) |
Urine ACR category (%) | — | — | ||
Normoalbuminuria (<30 mg/g) | — | 57.2 | — | 41.6 |
Microalbuminuria (30–299 mg/g) | — | 30.0 | — | 30.9 |
Macroalbuminuria (≥300 mg/g) | — | 12.8 | — | 27.5 |
Plasma C-reactive protein (μg/mL), median (IQR) | 1,913 | 2.0 (0.6–4.7) | — | — |
Medications (%) | ||||
Metformin | 1,908 | 85.4 | — | — |
Sulfonylurea | 1,906 | 49.4 | — | — |
DPP-4 inhibitor | 1,907 | 7.9 | — | — |
Insulin | 1,905 | 26.9 | 346 | 44.5 |
RAS blocker | 1,901 | 59.8 | 346 | 59.2 |
Aspirin | 1,905 | 21.9 | — | — |
. | Discovery . | Replication . | ||
---|---|---|---|---|
. | n . | Value . | n . | Value . |
Age (years) | 1,915 | 57.1 ± 10.8 | 346 | 54.7 ± 12.6 |
Male sex (%) | 1,915 | 50.2 | 346 | 59.2 |
Ethnicity | 1,915 | 346 | ||
Chinese | — | 51.2 | — | 54.6 |
Malay | — | 21.7 | — | 27.5 |
Asian Indian | — | 27.2 | — | 17.9 |
ASCVD history (%) | 1,914 | 7.7 | 346 | 15.0 |
Active smokers (%) | 1,911 | 8.7 | 346 | 14.7 |
Diabetes duration (years) | 1,906 | 11.0 ± 8.9 | 345 | 11.8 ± 8.5 |
BMI (kg/m2) | 1,911 | 27.7 ± 5.2 | 346 | 27.3 ± 5.7 |
HbA1c (%) | 1,915 | 7.8 ± 1.3 | 346 | 8.9 ± 2.2 |
HbA1c (mmol/mol) | — | 62 ± 10 | — | 74 ± 18 |
Blood pressure (mmHg) | 1,915 | 340 | ||
Systolic | — | 140 ± 18 | — | 138 ± 19 |
Diastolic | — | 79 ± 9 | — | 79 ± 12 |
Mean arterial pressure | — | 99 ± 11 | — | 98 ± 12 |
eGFR (mL/min/1.73 m2) | 1,915 | 89 ± 23 | 346 | 84 ± 30 |
Urine ACR (mg/g), median (IQR) | 1,904 | 21 (6–83) | 327 | 55 (12–375) |
Urine ACR category (%) | — | — | ||
Normoalbuminuria (<30 mg/g) | — | 57.2 | — | 41.6 |
Microalbuminuria (30–299 mg/g) | — | 30.0 | — | 30.9 |
Macroalbuminuria (≥300 mg/g) | — | 12.8 | — | 27.5 |
Plasma C-reactive protein (μg/mL), median (IQR) | 1,913 | 2.0 (0.6–4.7) | — | — |
Medications (%) | ||||
Metformin | 1,908 | 85.4 | — | — |
Sulfonylurea | 1,906 | 49.4 | — | — |
DPP-4 inhibitor | 1,907 | 7.9 | — | — |
Insulin | 1,905 | 26.9 | 346 | 44.5 |
RAS blocker | 1,901 | 59.8 | 346 | 59.2 |
Aspirin | 1,905 | 21.9 | — | — |
Data are mean ± SD unless otherwise indicated. DPP-4, dipeptidyl peptidase 4; RAS, renin-angiotensin system. —, no data.
In the discovery cohort, 91 incident ESKD events (73 participants progressed to eGFR <15 mL/min/1.73 m2, 17 to dialysis, and 1 to renal death) were identified during a median of 7.1 (IQR 6.8–7.6) years of follow-up. In the replication cohort, 46 incident ESKD events (43 participants progressed to eGFR <15 mL/min/1.73 m2, 2 to dialysis, and 1 to renal death) were identified during a median of 5.0 (IQR 4.0–6.3) years of follow-up. Participant characteristics stratified by the occurrence of ESKD are presented in Supplementary Table 3.
Associations Between Tryptophan-Kynurenine Metabolites and Risk of Progression to ESKD
In the discovery cohort, participants who progressed to ESKD had a lower level of tryptophan and higher KTR at baseline than their counterparts without events (Supplementary Table 4). As shown in Fig. 1, a high level of tryptophan was associated with a low risk of ESKD (unadjusted hazard ratio [HR] 0.65 [95% CI 0.55, 0.77] per 1 SD), while a high KTR was associated with an increased risk (unadjusted HR 1.48 [1.23, 1.79]). Adjustment for demographic and cardiorenal risk factors, including eGFR and urine ACR, did not materially change the strengths of these associations (adjusted HRs 0.62 [0.51, 0.75] and 1.48 [1.20, 1.84], respectively). When analyzed as a categorical variable, participants with tryptophan levels in the highest tertile had 69% lower adjusted risk for ESKD (adjusted HR 0.31 [0.17, 0.58]), while those with KTR in the highest tertile had a 3.15-fold [1.75, 5.64] increased risk compared with those in the lowest tertile (Supplementary Fig. 2).
Association between plasma tryptophan-kynurenine metabolite (per 1 SD) and the risk for ESKD (forest plot). A total of 1,871 participants (90 ESKD events) in the discovery cohort and 338 participants (43 ESKD events) in the replication cohort were included in the multivariable model.
Association between plasma tryptophan-kynurenine metabolite (per 1 SD) and the risk for ESKD (forest plot). A total of 1,871 participants (90 ESKD events) in the discovery cohort and 338 participants (43 ESKD events) in the replication cohort were included in the multivariable model.
Baseline kynurenic acid levels were lower in participants with incident ESKD (Supplementary Table 4). Kynurenic acid levels were inversely associated with risk of ESKD in both univariable (unadjusted HR 0.79 [95% CI 0.64, 0.97]) and multivariable models (adjusted HR 0.74 [0.60, 0.91]). The association between xanthurenic acid and ESKD was not statistically significant in the univariable model (unadjusted HR 0.86 [0.70, 1.05]). Adjustment for clinical risk factors strengthened the association (adjusted HR 0.74 [0.60, 0.91]) (Fig. 1). Consistent outcomes were obtained when these two metabolites were analyzed as categorical variables (Supplementary Fig. 2).
A high level of 3-hydroxykynurenine was associated with an increased risk for ESKD in the univariable model. The association was markedly attenuated after adjustment for clinical risk factors. Plasma kynurenine was not significantly associated with ESKD in either the univariable or multivariable analyses.
The analytical outcomes in the replication cohort were generally consistent with findings in the discovery cohort (Fig. 1 and Supplementary Fig. 2). Specifically, high levels of tryptophan, kynurenic acid, and xanthurenic acid were independently associated with a low risk for ESKD (adjusted HRs 0.55 [95% CI 0.39, 0.79], 0.53 [0.38, 0.75], and 0.51 [0.37, 0.71], respectively). A high KTR was associated with an increased risk for ESKD in the univariable model (unadjusted HR 1.40 [1.05, 1.86]), but the strength of the association did not reach statistical significance after adjustment for clinical risk factors (adjusted HR 1.29 [0.89, 1.87]).
Sensitivity Analyses
Consistent outcomes were obtained when we modeled metabolite concentrations directly and adjusted baseline eGFR as a covariate (Supplementary Fig. 3). Similar outcomes were obtained when the outcome was defined as a composite of ESKD and doubling of serum creatinine (Supplementary Fig. 4). Among participants with no ESKD events, 126 and 31 nonrenal deaths were identified in the discovery and replication cohorts, respectively. Fine and Gray competing risk regression showed that the outcomes remained consistent after considering nonrenal death as a competing risk (Supplementary Fig. 5). Exploratory analysis suggested that the association between plasma metabolites and the risk of ESKD was generally consistent across CKD status (eGFR <60 vs. ≥60 mL/min/1.73 m2), albuminuria category (urine ACR <30 mg/g, 30–299 mg/g, and ≥300 mg/g), and ethnic subgroups (Supplementary Tables 5–7). Additional adjustment for C-reactive protein, aspirin use, and antidiabetic medications in the multivariable model did not substantially alter the outcomes in the discovery cohort (Supplementary Table 8).
Association Between Tryptophan-Kynurenine Pathway Metabolites and eGFR Decline
Rapid eGFR decline is the common pathway leading to ESKD (17). In 1,441 participants with at least three eGFR measurements in >2 years of follow-up in the discovery cohort, a 1-SD increment in tryptophan, kynurenic acid, and xanthurenic acid was associated with a median of 0.24 (IQR 0.08–0.40), 0.20 (0.04–0.36), and 0.22 (0.05–0.38) mL/min/1.73 m2 slower eGFR declines per year, respectively, after adjustment for clinical risk factors. A 1-SD increment in KTR was associated with 0.29 (0.45–0.12) mL/min/1.73 m2 faster eGFR decline per year. Similar results were obtained in the replication cohort (Fig. 2 and Supplementary Fig. 6).
Association between plasma tryptophan-kynurenine metabolites (per 1 SD) and eGFR slope (mL/min/1.73 m2 per year). A positive slope indicates a slower eGFR decline, while a negative slope indicates a faster decline with an increase in the plasma metabolite or ratio. A total of 1,441 participants from the discovery cohort and 336 participants from the replication cohort with at least three eGFR measurements in >2 years of follow-up were included in this analysis.
Association between plasma tryptophan-kynurenine metabolites (per 1 SD) and eGFR slope (mL/min/1.73 m2 per year). A positive slope indicates a slower eGFR decline, while a negative slope indicates a faster decline with an increase in the plasma metabolite or ratio. A total of 1,441 participants from the discovery cohort and 336 participants from the replication cohort with at least three eGFR measurements in >2 years of follow-up were included in this analysis.
We identified 256 and 100 participants with rapid kidney function decline in the discovery and replication cohorts, respectively. Logistic regression models showed that participants with a high level of tryptophan, kynurenic acid, and xanthurenic acid had significantly lower odds of rapid kidney function decline, while those with a high KTR had significantly higher odds (Supplementary Fig. 7).
Association Between Plasma Kynurenic Acid and the Risk for ESKD in CRIC
In 1,312 participants with diabetes and a baseline eGFR ≥30 mL/min/1.73 m2 (Supplementary Table 9), 486 ESKD events were registered during a median of 6.1 (IQR 2.8–11.0) years of follow-up. Kynurenic acid interacted with macroalbuminuria (urine ACR ≥300 mg/g, yes or no) in association with ESKD (P = 0.029 for interaction). A multivariable Cox regression model showed that kynurenic acid was not associated with risk of ESKD in 489 participants with macroalbuminuria (adjusted HR 1.04 [95% CI 0.93, 1.17]). However, in those with urine ACR <300 mg/g, a 1-SD increment in kynurenic acid was associated with a 26% lower risk for ESKD (adjusted HR 0.74 [0.62, 0.88]) (Supplementary Table 10). Kynurenic acid was associated with ESKD in both participants with normoalbuminuria and microalbuminuria (adjusted HRs 0.69 [0.50, 0.96] and 0.75 [0.60, 0.96], respectively; P = 0.63 for interaction).
Conclusions
We have two main findings in this prospective study. First, increasing concentrations of plasma tryptophan are inversely associated with risk for progression to ESKD, while a high KTR is positively associated with the risk for progressive loss of kidney function. Second, high levels of kynurenic acid and xanthurenic acid are associated with a low risk of incident ESKD independent of traditional cardiorenal risk factors, including eGFR and albuminuria. Our data suggest that 1) accelerated catabolism of tryptophan in the kynurenine pathway may be involved in the pathophysiological pathway underpinning progressive loss of kidney function and 2) shunting the kynurenine pathway to kynurenic acid branch may potentially reduce the risk of DKD progression.
Accelerated Tryptophan Catabolism and DKD Progression
The inverse association between plasma tryptophan and rapid loss of kidney function has been observed in patients with diabetes and CKD (19). A case-control study in patients with diabetes found that participants with a high level of tryptophan had a low odds of future ESKD (20). Additionally, a high KTR has been associated with an increased risk for new-onset CKD in the general population (11,12). In the context of the literature, our findings of an inverse association between tryptophan and ESKD and positive association between KTR and ESKD in patients with diabetes add new evidence to support that an accelerated catabolism of tryptophan in the kynurenine pathway may be a marker and/or an effector of progressive DKD.
The tryptophan-kynurenine pathway is activated under inflammatory conditions (21). Inflammation is central in the pathophysiological network driving progressive loss of kidney function in patients with diabetes (22). An elegant study found that a tumor necrosis factor–related inflammation signature is a strong predictor of ESKD in people with diabetes (23). Hence, the mechanistic driver of kynurenine pathway activation in patients with progressive DKD may be the elevated inflammation tone. We did not measure tumor necrosis factor α–related biomarkers in our study. We observed a moderate but statistically significant correlation between KTR and C-reactive protein in the discovery cohort (Supplementary Table 2). Nevertheless, additional adjustment for C-reactive protein did not attenuate the association between KTR and ESKD (Supplementary Table 8). Hence, elevated systemic inflammation may not convincingly explain the association between KTR and the risk for ESKD.
The biological implication of kynurenine pathway activation in progressive DKD remains to be elucidated. Tryptophan is metabolized in the kynurenine pathway to produce the oxidized form of NAD (24). Because NAD depletion is one of the main contributors to the pathogenesis of renal disease, a large portion of metabolites in the kynurenine pathway may be directed to the NAD branch to replenish cellular NAD (24). Of note, modulating IDO activity yielded opposing outcomes in animal models. Inhibition of IDO by 1-methyltryptophan worsened crescentic glomerulonephritis in renal tissue (25), suggesting that IDO activation might have an anti-inflammatory effect. Similarly, overexpression of IDO or treatment with IDO agonist mitigated renal injury in IgA nephropathy in mice (26). In contrast, another preclinical study found that IDO activation activated Wnt/β-catenin signaling and promoted kidney fibrosis (27). We are unable to reconcile these controversial findings, but the differences in animal models used in these studies may partly account for the discrepancies.
Kynurenic Acid, Xanthurenic Acid, and Risk for Progression to ESKD
The independent association of kynurenic acid and xanthurenic acid with the risk for ESKD is a novel finding. Kynurenic acid is a known anti-inflammatory and antioxidant compound (28,29). Circulating kynurenic acid has been found to exert neuroprotection and cardiac protection (30,31). Of note, preclinical studies suggested that kynurenic acid may also exert renal protection. For example, a mouse model study found that supplementing kynurenic acid or inhibiting kynurenine 3-monooxygenase to shunt the kynurenine pathway toward kynurenic acid production reduced kidney injury induced by ischemia reperfusion (32). Moreover, a preclinical study suggested that kynurenic acid might also have natriuretic and diuretic effects. Intravenous administration of kynurenic acid increased renal blood flow and urine sodium excretion in Sprague-Dawley and spontaneous hypertensive rats (33). Our finding of an inverse association between circulating kynurenic acid and the risk for progression to ESKD is consistent with these preclinical data, suggesting that an increased level of kynurenic acid may have potential renal protective properties.
Xanthurenic acid is also an antioxidant that promotes the generation of reduced glutathione (34). It may also conjugate with sulfate or glucoside in proximal tubules to form xanthurenic acid 8-O-β-D-glucoside and xanthurenic acid 8-O-sulfate. Both compounds induced a sustained increase in urine flow and sodium excretion when injected into rats, in part by a nitric oxide–dependent mechanism (35,36). Future studies are warranted to validate whether this compound may protect against kidney failure.
Production of kynurenic acid and xanthurenic acid was mediated by the same enzyme, kynurenine aminotransferase (KAT) (10). Downregulation of KAT expression and reduced kynurenic acid production in carotid plaques have been associated with increased inflammation and an increased risk of cerebrovascular events (7). Interestingly, KAT expression is under control of peroxisome proliferator–activated receptor γ coactivator 1α and is highly correlated with mitochondrial content (30). As mitochondria dysfunction is a hallmark of progressive DKD (37), it will be of interest to study KAT expression and mitochondrial function in diabetic kidneys in the future.
Relationship of Kynurenine and 3-Hydroxykynurenine With DKD Progression
Early studies suggested that an elevated level of kynurenine drives mesangial cell proliferation (38), while 3-hydroxykynurenine promotes the generation of superoxide anions, leading to accelerated apoptosis and endothelial dysfunction (39). We did not observe a significant association between kynurenine and renal outcome defined as either ESKD or rapid eGFR decline. 3-Hydroxykynurenine was predictive of DKD progression in univariable analysis, but the association was diminished after accounting for clinical risk factors. Thus, our data do not support a role of kynurenine or 3-hydroxykynurenine as a driver of DKD progression, although serum accumulation of these two metabolites has been observed in individuals with CKD (10).
Strengths and Limitations
The unique Asian population with a high susceptibility to DKD and the long-term follow-up enabled us to select ESKD as the primary outcome. We adopted a discovery and replication approach in study design and performed several sensitivity analyses to enhance the robustness of our data. We also validated one of the main findings in an independent cohort with different ethnic backgrounds, suggesting that our data are potentially generalizable. Several important weaknesses should be highlighted. First, we did not profile the full spectrum of the kynurenine pathway. Future studies are needed to examine the role of anthranilic acid, picolinic acid, and quinolinic acid in progressive DKD. Second, we are unable to infer causality because of the nature of the study. Additionally, residual confounding is inevitable, although we considered the main cardiorenal risk factors in our analyses. Third, because kidney impairment is an established risk factor for ESKD, while kidney insufficiency leads to plasma retention of kynurenine-related metabolites even before overt eGFR reduction (40), it is challenging to dissect the complex relationship between plasma metabolites and CKD progression in observational studies. Therefore, further studies in animal models are equally important to corroborate findings from our clinical study. Fourth, the number of ESKD events in the discovery and replication cohorts were relatively small. The analyses after stratification by CKD, albuminuria category, and ethnicity were underpowered and should only be considered exploratory. Fifth, our findings on tryptophan, KTR, and xanthurenic acid were not validated in the external cohort. Finally, the outcomes from the three cohorts may not be directly comparable because of differences in population characteristics and measurements of clinical variables.
In conclusion, our data suggest that accelerated catabolism of tryptophan in the kynurenine pathway may be involved in progressive loss of kidney function in patients with type 2 diabetes. Shunting the tryptophan catabolic pathway to the kynurenic acid branch may potentially slow DKD progression. Our findings may prompt future studies to further characterize the role of the kynurenine pathway in progressive DKD.
This article contains supplementary material online at https://doi.org/10.2337/figshare.24150249.
J.-J.L. and J.C. were equal contributors to the work.
Article Information
Acknowledgments. The authors thank the participants in the SMART2D and KTPH-DKD cohorts and all staff in the clinical research unit at Khoo Teck Puat Hospital Singapore for their contributions to the study. The authors also thank CRIC participants and investigators for their contribution to this work by sharing their data through the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) data repository. The CRIC study was conducted by the CRIC Investigators and supported by NIDDK. The data from the CRIC study reported here were supplied by the NIDDK Central Repository. This manuscript was not prepared in collaboration with investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repository, or the NIDDK.
Funding. This work was funded by Khoo Teck Puat Hospital STAR grants 20201 and 23201 and Singapore National Medical Research Council grants MOH-000066, MOH-000714-01, and MOH-001327-02.
The funders had no role in the study design, data analysis, manuscript writing, or decision for publication.
Duality of Interest. B.R.K. received consulting fees from Launch Therapeutics. K.S. received consulting fees from Otsuka, received honoraria from Pfizer, served on the CARA data monitoring board, received support from the American Society of Transplantation for attending meetings, and is the scientific founder of SygnaMap. No other potential conflicts of interests relevant to this article were reported.
Author Contributions. J.-J.L. designed the study and performed the data analysis. J.-J.L. and J.C. drafted the manuscript. J.C., H.N.W., S.L., R.L.G., J.L., Y.M., H.Z., L.S.L., K.A., Y.M.S., J.-P.K., T.S., C.F.S., K.S., B.R.K., and S.C.L. collected data and contributed important intellectual knowledge. All authors revised the manuscript critically for important intellectual content and approved the publication of the manuscript. S.C.L. is the guarantor of this work and, as such, had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.